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December 22, 2021

Agile in analytics, time series forecasting, visualizing dataframe transforms

  • I enjoy Taylor Browlow's writing on data science in industry. How to make agile actually work for analytics, discusses what we all probably struggle with: Agile for data science teams. I've leveraged Agile (mostly Scrum) on my data science and data engineering teams for many years now. It's not a perfect fit, but the benefits have outweighed the costs.

    The article reimagines the four core Agile tenets

    • Individuals and interactions over processes and tools
    • Working software over comprehensive documentation
    • Customer collaboration over contract negotiation
    • Responding to change over following a plan

    in an analytics context

    • Decisions over dashboards
    • Functional analysis over perfect outputs
    • Sharing data over gatekeeping data
    • Individuals and interactions over processes and tools
  • This advice from James Clear: revise, revise, revise.

    "The difference between good and great is often an extra round of revision.

    The person who looks things over a second time will appear smarter or more talented, but actually is just polishing things a bit more.

    Take the time to get it right. Revise it one extra time."

  • There is a sizable market (and not enough expertise) in time series forecasting, so I'm always interested in what's developing in that space. ETNA is a new framework from the Tinkoff.ru Artificial Intelligence Center that I'm looking at.

  • VS Code continues to improve python support. They just released a new Python Extension.

  • A great read from meta on how they use AI to animate children's drawings.

  • Very straightforward guide to setting up your new MBPro with Conda and TensorFlow.

  • This came from a best-of list, and it's a wonderful list of 10 skills to develop with applications to both engineering and life. The note at the end about looking for systems struck me as a hard skill to develop, but impactful if you can learn it.

  • I don't know if it's actually useful, but this visualization of pandas dataframe transformations sure looks cool.

  • Overall I disagree with this author's premise -- namely that Web3 is Bullshit, but there are some good points, specifically that there are real issues regarding data ownership. It is a good, short read disputing all the Web3 hype.

  • Finally, this has nothing to do with data science or data engineering, but it was the best thing I read all week so I felt the need to share it. A plus-sized Jewish lady redneck died in El Paso on Saturday.

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